sifu 0.1.0

Enhanced Knowledge System for ELLMa

Sifu - Advanced Knowledge and NLP Support System

[](https://opensource.org/licenses/Apache 2.0 )

Sifu is an advanced knowledge and natural language processing system designed to enhance AI assistants with sophisticated context handling, real-time learning, and multi-language support.

🌟 Features

• Advanced NLP: Intent recognition and entity extraction
• Knowledge Management: Store and retrieve information with confidence scoring
• Context Awareness: Maintain conversation context and history
• Multi-language Support: Automatic language detection and translation
• Learning Engine: Improve responses through feedback
• RESTful API: Easy integration with existing systems
• Modular Architecture: Extensible design for custom components

🚀 Quick Start

Prerequisites

• Python 3.8+
• Poetry for dependency management
• (Optional) GPU for better ML performance

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/sifu.git
   cd sifu
   

2. Install dependencies using Poetry:

   poetry install
   

   Or install with pip:

   pip install -r requirements.txt
   

3. Install language models (for NLP features):

   python -m spacy download en_core_web_sm
   

Basic Usage

from sifu import Sifu
import asyncio

async def main():
    # Initialize Sifu
    sifu = Sifu()
    
    # Process a query
    response = await sifu.process_query("Hello, what can you do?")
    print(f"Response: {response['text']}")
    
    # Add knowledge
    await sifu.knowledge_base.add_entry(
        content="Sifu is an AI assistant",
        tags=["introduction"],
        language="en"
    )

if __name__ == "__main__":
    asyncio.run(main())

📚 Documentation

For detailed documentation, please visit our documentation site.

Key Components

• Knowledge Base: Store and retrieve information
• Context Management: Handle conversation state
• Intent Matcher: Understand user intents
• Learning Engine: Improve through feedback
• API Reference: REST API documentation

🧪 Testing

Run the test suite:

# Install test dependencies
poetry install --with test

# Run tests
pytest tests/ -v

# Run with coverage
pytest --cov=sifu --cov-report=term-missing

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide for details.

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

📞 Contact

For questions or support, please open an issue or contact [e-mail].

---

Made with ❤️ by Your Name

for ELLMa

Core Components

1. Knowledge Management System

• SimpleKnowledgeBase: Core knowledge storage with confidence scoring and continuous learning
• Context Manager: Handles conversation history and context awareness
• Response Generator: Multi-strategy response generation with fallback mechanisms

2. Advanced Features

• Real-time Learning: Learns from interactions and feedback
• Multi-language Support: Built-in support for multiple languages
• User Profiles: Personalized knowledge and preferences
• External API Integration: Access to real-time information
• FastAPI-based API: Easy integration with other systems

Documentation

Architecture

graph TD
    A[User Query] --> B[Context Manager]
    B --> C{Query Type?}
    C -->|Knowledge| D[Knowledge Base]
    C -->|External Data| E[API Integrations]
    C -->|Conversation| F[Conversation History]
    D --> G[Response Generator]
    E --> G
    F --> G
    G --> H[Response + Learning]
    H --> I[Update Knowledge]
    I --> J[User Feedback]
    J --> H

Key Algorithms

1. Context-Aware Response Generation

   • Uses conversation history and user context
   • Implements attention mechanisms for better context understanding
   • Learn more

2. Pattern Matching with NLP

   • Advanced NLP techniques for intent recognition
   • Support for fuzzy matching and semantic similarity
   • Learn more

3. Real-time Learning Loop

   • Continuous learning from interactions
   • Confidence-based knowledge updates
   • Learn more

4. Multi-language Processing

   • Language detection and translation
   • Culture-aware responses
   • Learn more

Getting Started

Prerequisites

• Python 3.8+
• Poetry for dependency management
• Redis (for caching and real-time features)

Installation

# Clone the repository
git clone https://github.com/yourusername/sifu.git
cd sifu

# Install dependencies
poetry install

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

# Run the development server
poetry run uvicorn sifu.api:app --reload

API Documentation

Once the server is running, access the interactive API documentation at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Usage Examples

Basic Usage

from sifu import Sifu

# Initialize Sifu
sifu = Sifu()

# Process a query
response = sifu.process_query("What's the weather like today?")
print(response)

# Provide feedback
sifu.provide_feedback(
    question="What's the weather like today?",
    rating=1,
    better_response="The current weather is sunny with a temperature of 22°C."
)

Advanced Usage with Custom Knowledge Sources

from sifu import Sifu
from sifu.knowledge import WebSource, APISource

# Initialize with custom knowledge sources
sifu = Sifu(
    knowledge_sources=[
        WebSource(),
        APISource("weather_api", "https://api.weatherapi.com/v1")
    ]
)

# Process a query with context
response = sifu.process_query(
    "What's the weather in Tokyo?",
    context={"user_id": "123", "language": "en"}
)
print(response)

Configuration

Configuration is handled through environment variables. Copy .env.example to .env and adjust the settings as needed.

# Database
DATABASE_URL=sqlite:///./sifu.db
REDIS_URL=redis://localhost:6379/0

# API Keys (if using external services)
OPENAI_API_KEY=your_openai_key
WEATHER_API_KEY=your_weather_api_key

# Application Settings
DEBUG=True
LOG_LEVEL=INFO

Contributing

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/your-feature)
5. Create a new Pull Request

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Acknowledgements

• Built with ❤️ for the ELLMa project
• Uses FastAPI for the web framework
• Integrates with various NLP and ML libraries